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- W1584709881 abstract "There has been a recent interest in segmenting action sequences into meaningful parts (action primitives) and to model actions on a higher level based on these action primitives. Unlike previous works where action primitives are defined a-priori and search is made for them later, we present a sequential and statistical learning algorithm for automatic detection of the action primitives and the action grammar based on these primitives. We model a set of actions using a single HMM whose structure is learned incrementally as we observe new types. Actions are modeled with sufficient number of Gaussians which would become the states of an HMM for an action. For different actions we find the states that are common in the actions which are then treated as an action primitive." @default.
- W1584709881 created "2016-06-24" @default.
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- W1584709881 date "2009-01-01" @default.
- W1584709881 modified "2023-09-25" @default.
- W1584709881 title "Primitive Based Action Representation and Recognition" @default.
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- W1584709881 doi "https://doi.org/10.1007/978-3-642-02230-2_4" @default.
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